Neurobiologically-Based Subtyping of Multi-Cohort Samples with MDD and PTSD Symptoms

具有 MDD 和 PTSD 症状的多队列样本的基于神经生物学的亚型

• 批准号: 10609903
• 负责人: RAJENDRA A MOREY
• 金额: $ 55.95万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-15 至 2026-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10609903
• 关键词: Age; Algorithms; Arousal; Attention; Behavior; Biological Testing; Brain; Brain region; Categories; Characteristics; Chronic; Classification; Clinical; Collaborations; Control Groups; Coupled; Data; Data Set; Development; Diagnosis; Diagnostic; Dimensions; Disease; Disease model; Dorsal; Exposure to; Fright; Functional Magnetic Resonance Imaging; Future; Gender; Goals; Heterogeneity; Hybrids; Individual; Infrastructure; Life Stress; Major Depressive Disorder; Maps; Medical; Mental disorders; Modeling; Modernization; Neurobiology; Participant; Patients; Pattern; Population; Post-Traumatic Stress Disorders; Recording of previous events; Research; Research Domain Criteria; Resources; Rest; Sampling; Severity of illness; Sleep disturbances; Stress; Stressful Event; Symptoms; Syndrome; Technology; Testing; Time; Trauma; algorithm training; biobank; biotypes; brain circuitry; brain dysfunction; cohort; comorbidity; depressive symptoms; disease classification; effective therapy; experience; interest; large datasets; learning strategy; neural; neural circuit; neurobiological mechanism; neuropsychiatric symptom; novel; patient subsets; pediatric trauma; precision medicine; psychiatric comorbidity; psychological trauma; reward processing; supervised learning; symptom cluster; therapy design; translational neuroscience; trauma exposure; unsupervised learning

ABSTRACT Significant symptom overlap and high rates of co-occurrence between syndromes of posttraumatic stress disorder (PTSD) and major depressive disorder (MDD) call into question whether the two are distinct disorders. The onset and course of both syndromes are strongly influenced by environmental variables. We hypothesize that a continuum of life stress or adversity and an independent continuum of psychological trauma conspire to influence the onset of PTSD and MDD (where at least one trauma exposure is required for PTSD). Our overarching goal is to identify and compare neural signatures of MDD, PTSD, symptom features common to PTSD and MDD, and heretofore unrecognized neurobiologically-defined syndromes. Therefore, we plan to investigate neural signatures with supervised learning, and to identify biotypes that cut across disorders (PTSD and MDD) with unsupervised learning, an approach that can better explain contributions of trauma, stressful life events, and disease characteristics than possible with DSM-disorders. Rather than subtyping patients on the basis of clinical symptoms or DSM-defined diagnoses, our goal is to identify distinct clusters of neurobiological subtypes with disrupted neural signatures derived from resting-state fMRI. In Aim 1 we propose to train algorithms with supervised learning to detect neural signatures from resting fMRI data that can classify DSM diagnosis of comorbid PTSD and MDD, PTSD only, MDD only, and Controls (no psychiatric disorder). The analysis will be performed separately with MDD and Control groups who experienced criterion-A trauma or stressful life events, and those who did not. In Aim 2, we plan to use supervised learning in MDD and PTSD patients to identify neural signatures from resting-state fMRI data associated with four trans-diagnostic symptoms that include disrupted sleep, irritability, concentration difficulties, and loss of interest. In Aim 3, we propose to apply unsupervised learning methods to identify novel biotypes associated with specific symptoms or symptom clusters. The algorithms will employ rsfMRI features in patients with (1) PTSD only, (2) MDD only and (2) across PTSD, MDD, and comorbid PTSD+MDD patients in order to identify potential trans-diagnostic biotypes that cut across DSM boundaries. We will investigate associations of diagnosis-specific and trans- diagnostic biotypes derived from unsupervised learning with stressful life events, trauma exposure, developmental stage at time of exposure, psychiatric comorbidities, medical comorbidities, illness chronicity, illness severity, gender, and age. The overlapping and intersecting patterns that maps circuit disruption to psychiatric syndromes presents a daunting challenge in designing treatments that intervene at the circuit level. Developing a neurobiologically-based nosology that maps to clinical symptoms and syndromes represents a major advance in translational neuroscience. The advent of modern brain stimulation technology offers an unprecedented possibility of intervening at the circuit level with precision medicine strategies.

抽象的 创伤后压力综合征之间的明显症状重叠和高率的同发生率 疾病（PTSD）和主要抑郁症（MDD）质疑这两者是否是不同的疾病。 两种综合征的开始和过程都受到环境变量的强烈影响。我们假设 生活压力或逆境的连续性以及心理创伤的独立连续性共谋 影响PTSD和MDD的发作（其中至少需要一种创伤PTSD的创伤）。我们的 总体目标是识别和比较MDD，PTSD，症状特征的神经特征 PTSD和MDD，以及迄今未识别的神经生物学定义的综合征。因此，我们计划 通过有监督的学习调查神经信号，并确定跨疾病的生物型（PTSD） 和MDD）有无监督的学习，这种方法可以更好地解释创伤的贡献 生命事件和疾病特征比DSM抑制剂所能。而不是使患者在 临床症状或DSM定义诊断的基础，我们的目标是确定 神经生物学亚型的神经信号破坏，从静止状态fMRI衍生出。在AIM 1中，我们建议 通过有监督的学习来培训算法，以检测可以通过静止的fMRI数据进行分类的神经信号 DSM诊断合并症PTSD和MDD，仅PTSD，仅MDD和对照（无精神疾病）。 分析将与经历过标准的MDD和对照组分别进行 - 强调生活事件，以及那些没有的人。在AIM 2中，我们计划在MDD和PTSD中使用受监督的学习 患者从静止状态fMRI数据中识别与四个反诊断相关的静止状态fMRI数据 症状包括睡眠中断，易怒，集中困难和失去兴趣。在AIM 3中，我们 建议采用无监督的学习方法来识别与特定症状相关的新型生物型 或症状群。该算法将仅在（1）PTSD患者中使用RSFMRI功能，（2）MDD （2）在PTSD，MDD和合并症PTSD+MDD患者中，以识别潜在的反式诊断 跨DSM边界切割的生物型。我们将研究诊断特异性和反式的关联 诊断性生物型从无监督的学习中，带有压力性的生活事件，创伤暴露， 暴露时的发育阶段，精神病合并症，医学合并症，疾病慢性， 疾病的严重程度，性别和年龄。将电路中断映射到的重叠和相交模式 精神病综合症在设计在电路级别进行干预的治疗方面提出了艰巨的挑战。 开发基于神经生物学的细胞学，该细胞学局限于临床症状和综合症代表 转化神经科学的重大进展。现代大脑刺激技术的出现提供了 在电路一级与精确医学策略进行干预的前所未有的可能性。